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Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow
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deep_sort yolov3_deep_sort May 7, 2018
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LICENSE Change LICENSE from MIT to GPL-3.0 Sep 11, 2018 add diagram Aug 22, 2019 yolov3_deep_sort May 7, 2018 Update Mar 21, 2019 repaired a bug May 16, 2018
yolo.pyc yolov3_deep_sort May 7, 2018
yolov3.cfg yolov3_deep_sort May 7, 2018

QQ group: 姿态检测&跟踪 781184396


Thanks for these projects, this work now is support tiny_yolo v3 but only for test, if you want to train you can either train a model in darknet or in the second following works. It also can tracks many objects in coco classes, so please note to modify the classes in besides, you also can use camera for testing.

Quick Start

  1. Download YOLOv3 or tiny_yolov3 weights from YOLO website.Then convert the Darknet YOLO model to a Keras model. Or use what i had converted (yolo.h5 model file with tf-1.4.0) , put it into model_data folder

  2. Run YOLO_DEEP_SORT with cmd :

  3. (Optional) Convert the Darknet YOLO model to a Keras model by yourself:

 please download the weights at first from yolo website. 
 python yolov3.cfg yolov3.weights model_data/yolo.h5


The code is compatible with Python 2.7 and 3. The following dependencies are needed to run the tracker:


Additionally, feature generation requires TensorFlow-1.4.0.

Training the model

To train the deep association metric model on your datasets you can reference to approach which is provided as a separate repository.

Be careful that the code ignores everything but person. For your task do not forget to change :

[deep_sort_yolov3/] Lines 100 to 101 :

      if predicted_class != 'person' : 


You can use any Detector you like to replace Keras_version YOLO to get bboxes , for it is to slow !

Model file model_data/mars-small128.pb need by deep_sort had convert to tensorflow-1.4.0

Deep sort 程序结构见 “model_data/DeepSORT”,如有错误欢迎联系修改。

Test only

Speed : when only run yolo detection about 11-13 fps , after add deep_sort about 11.5 fps (GTX1060 6G)

Test result video : generated by this project

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